Main Page

From Thalesians

The Thalesians

Images from Thalesians events from around the world over the past 6 years

The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.

Blog / See our new Thalesians blog / Book / Buy our new book, Trading Thalesians - What the ancient world can teach us about trading today (Palgrave Macmillan) by the Thalesians co-founder, Saeed Amen & foreword by founder, Paul Bilokon

Founding / The group was founded in Sep 2008, by Paul Bilokon (then a quantitative analyst at Lehman Brothers specialising in foreign exchange, and a part-time researcher at Imperial College), and two of his friends and colleagues: Matthew Dixon (then a quantitative analyst at Deutsche Bank) and Saeed Amen (then a quantitative strategist at Lehman Brothers).

The opening of Level39 in 2013 by Mayor Boris Johnson

The Thalesians are also now a member of Level39 - Europe's largest technology accelerator for finance, retail, cyber-security and future cities technology companies​

Events / Research / Consulting

Events / The Thalesians were originally based in London, UK. In Jan 2011, the organisation became truly global when Matthew Dixon brought it to the United States where he runs the Thalesians NYC seminars with New York Leader Harvey Stein. Attila Agod is the Budapest Leader for our Thalesians Budapest seminars. We are currently in the process of expanding our seminars to Prague and running more workshops.

Research / In late 2013, we started published ground breaking quant strategy notes. Our effort is lead by Saeed Amen, using nearly a decade of his experience both creating and later trading systematic trading models in FX at major investment banks. Visit Research for more.

Consulting / In 2014, we started offering bespoke quant consulting services in markets, signing up our first client, a major US hedge fund and RavenPack, a major news data vendor. Our services includes the creation of bespoke systematic trading models and other quant analysis of financial markets, such as currency hedging and FX transaction cost analysis (TCA). Visit Consulting for more.

Our Philosophy

We are named after Thales of Miletus (Θαλῆς ὁ Μιλήσιος), a pre-Socratic Greek philosopher who lived in ca. 624 BC-ca. 546 BC. Thales was a mathematician and is familiar to many secondary school students for one of his theorems in geometry.

But more relevantly to us, he was one of the first users of options:

"Thales, so the story goes, because of his poverty was taunted with the uselessness of philosophy; but from his knowledge of astronomy he had observed while it was still winter that there was going to be a large crop of olives, so he raised a small sum of money and paid round deposits for the whole of the olive-presses in Miletus and Chios, which he hired at a low rent as nobody was running him up; and when the season arrived, there was a sudden demand for a number of presses at the same time, and by letting them out on what terms he liked he realised a large sum of money, so proving that it is easy for philosophers to be rich if they choose, but this is not what they care about." — Aristotle, Politics, 1259a.

The morale of this anecdote is that it is easy for philosophers to be rich if they choose; the famous Milesian went ahead and proved it.

We, the Thalesians, admire him for that. But we also share many of his values, for example his core belief that a happy man is defined as one "ὁ τὸ μὲν σῶμα ὑγιής, τὴν δὲ ψυχὴν εὔπορος, τὴν δὲ φύσιν εὐπαίδευτος" (who is healthy in body, resourceful in soul and of a readily teachable nature).

This wiki was created to serve as a source of information on quantitative finance, to collate references to various related resources, and to serve as a convergence point for the Thalesians, our colleagues and collaborators. It grew out of Paul Bilokon's finance wiki, which he started in February, 2007.

We believe that secrecy and fidelity are important in the world of finance. But we also acknowledge the power of information sharing in open societies. Let your business logic remain a closely guarded secret. But release everything else into the public domain. What goes around, comes around; this will ultimately spare you reinventing the wheel.

Venue

Registration

Abstract

We examine the "Low-Vol" effect across the capital structure by forming two portfolios of stocks and bonds issued by the same HY-rated companies that are risk-matched ex-ante, and track their performance post formation. We find very strong and persistent evidence that the bond portfolios outperform the matched stock portfolios in different periods, across all industries and credit ratings, in both US and European markets and even after fallen angels are excluded. Furthermore, at the company level bonds outperformed the stocks 70% of the time. Results are insensitive to the risk-matching approach used.

Speaker

Arik Ben Dor is a Managing Director and Head of Quantitative Equity Research at Barclays.

Over the past 15 years, Dr. Ben Dor oversaw large scale research projects in rates, credit, equities, and hedge funds used by the largest institutional investors globally, including central banks, Sovereign wealth funds, asset managers, insurance companies, pensions and hedge funds. He co-authored two books on quantitative investing in credit securities and over a dozen articles in leading industry journals such as the Journal of Portfolio Management, Journal of Fixed Income, Journal of Investment Management, and Journal of Alternative Investments. One of his articles received the Martello award for the 2007 best practitioner paper.

Dr. Ben Dor's research on ‘DTS (Duration Times Spread)’, a new approach to measuring the spread risk of corporate bonds and credit default swaps changed industry practices and was widely adopted by credit investors globally. In 2018, he was ranked 1st in the II All-America Fixed Income Research survey in the Quantitative Analysis category. Dr. Ben Dor also conducted research on ‘cloning’ hedge funds was the basis for several products and was awarded a U.S. patent.

His work on exploring the cross-asset relation between stocks and bonds was the basis for constructing systematic equity strategies such as momentum and ‘value’ based on credit signals, and the usage of equity derivatives for hedging high-yield bonds. His systematic strategies were adopted by some of the most prominent quantitative hedge funds and ‘long-only’ asset managers and were presented in leading industry conferences.

Prior to Barclays, Dr. Ben Dor worked at Lehman Brothers and Morgan Stanley. He holds a PhD in Finance from the Kellogg Business School at Northwestern University, and completed his B.A. and M.A. in Economics from Tel Aviv University, Cum Laude.

IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.

Venue

Registration

Abstract

Today’s financial markets are comprised of many coexisting trading venues. Traders often participate in multiple exchanges for the same assets. Fragmentation is a characteristic of virtually any asset class.

This paper develops a framework for decentralized markets with multiple exchanges and flexible trading strategies for any number of strategic agents and assets. The framework enables an explicit treatment of price impact.

We examine three classes of instruments that would be neutral if traders had no price impact or trading were centralized: (1) the creation of new trading venues for existing assets, (2) the introduction of new assets, and (3) innovation in the types of market clearing that decentralized trading enables.

Speaker

Marzena Rostek is the Lowell and Leila Robinson Professor of Economics at the University of Wisconsin-Madison. She holds a BA in Economics from the University of Warsaw (1999), a MSc from the Catholic University of Leuven (2000), an MPhil from the University of Amsterdam and Tinbergen Institute (2001), and a PhD from Yale University (2006). She was a postdoctoral research fellow at Nuffield College at Oxford University (2007-8).

Rostek has advanced modeling of financial markets with price impact, with special focus on the effects of fragmentation, market opacity, and apparently excessive financial innovation. Her work has suggested new possibilities for designing securities and exchanges to accomplish certain revenue and efficiency objectives.

Rostek is the recipient of several research and teaching awards. Her research has been supported by multiple grants from NSF, Net Institute, and WARF. Rostek is a member of Finance Theory Group and an Associate Editor at Econometrica, Journal of Economic Literature, Journal of European Economic Association, Economic Theory, and Economic Theory Bulletin.

IAQF-Thalesians Seminars

The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.

Venue

Meetup.com

Abstract

We consider market players with tail-risk-seeking behaviour as exemplified by the S-shaped utility introduced by Kahneman and Tversky. We argue that risk measures such as value at risk (VaR) and expected shortfall (ES) are ineffective in constraining such players. We show that, in many standard market models, product design aimed at utility maximization is not constrained at all by VaR or ES bounds: the maximized utility corresponding to the optimal payoff is the same with or without ES constraints. By contrast we show that, in reasonable markets, risk management constraints based on a second more conventional concave utility function can reduce the maximum S-shaped utility that can be achieved by the investor, even if the constraining utility function is only rather modestly concave. It follows that product designs leading to unbounded S-shaped utilities will lead to unbounded negative expected constraining utilities when measured with such conventional utility functions. To prove these latter results we solve a general problem of optimizing an investor expected utility under risk management constraints where both investor and risk manager have conventional concave utility functions, but the investor has limited liability. We illustrate our results throughout with the example of the Black--Scholes option market. These results are particularly important given the historical role of VaR and that ES was endorsed by the Basel committee in 2012--2013.

Speaker

Damiano Brigo (born Venice, Italy 1966) is an applied mathematician and Chair in Mathematical Finance at Imperial College London. He is known for research in filtering theory and mathematical finance.

Brigo started his work with the development, with Bernard Hanzon and Francois Le Gland (1998), of the projection filters, a family of approximate nonlinear filters based on the differential geometry approach to statistics, also related to information geometry. With Fabio Mercurio (2002–2003), he has shown how to construct stochastic differential equations consistent with mixture models, applying this to volatility smile modeling in the context of local volatility models. With Aurelien Alfonsi (2005), Brigo introduced new families of multivariate distributions in statistics through the periodic copula function concept. Since 2002, Brigo contributed to credit derivatives modeling and counterparty risk valuation, showing with Pallavicini and Torresetti (2007) how data implied non-negligible probability that several names defaulted together, showing some large default clusters and a concrete risk of high losses in collateralized debt obligations prior to the financial crisis of 2007–2008. This work has been further updated in 2010 leading to a volume for Wiley, while a volume on the updated nonlinear theory of valuation, including credit effects, collateral modeling and funding costs, has appeared in 2013. Overall Brigo authored more than seventy publications and co-authored the book Interest rate models: theory and practice for Springer-Verlag, that quickly became an international reference for stochastic dynamic interest rate modeling in finance. Brigo has been the most cited author in the technical section of the industry influential Risk Magazine in 2006, 2010 and 2012.

Venue

Meetup.com

Abstract

Quantum computing is a fast developing field of theoretical physics. Since larger problems are expected to be intractable on classical computers, there is much interest in solving them efficiently using quantum computers.
They provide exponentially faster factoring of integers and quadratically faster unordered search than with any other known classical algorithm.
Quantum computation concepts are based on the principles of quantum mechanics, such as superposition and entanglement. Although no efficient quantum computers exist yet, a number of experiments have been carried out in which quantum computational operations were executed on a very small number of quantum bits. In the last year IBM introduced a 50 qubit computer which became the most sophisticated one yet.

Quantum computers have defence, healthcare, energy, laboratory, web and finance applications, which include risk modelling, trading strategies, portfolio optimisation, asset pricing and hedging. Furthermore, machine learning benefits from quantum algorithms as well. Primary concepts of quantum cryptography and quantum internet have already been developed and can therefore be introduced.

Speaker

Sophie Bismuth is completing her postgraduate studies in mathematics at Imperial College London and has a physics degree from Moscow State University. There she completed her dissertation in theoretical physics and graduated from the department of quantum statistics and field theory. Sophie was a member of a research group at Skobeltsyn Institute of Nuclear Physics for a year, where she was working with the department of atomic physics and microelectronics and published her paper in radioelectronics. She has also worked at the Joint Institute for Nuclear Research with accelerators.

Venue

Meetup.com

Abstract

Tumours comprise not only cancer cells, but many other types of cells. These other cells are not in themselves cancerous (mutated) but work for the cancer helping it to thrive, creating a society of workers with different jobs to perform. Some of these worker cells are able to self-organise into patterns that make the cancer more likely to spread to other parts of the body. Understanding how these patterns emerge requires multi-scale mathematical modelling in order to pinpoint how tumours can be manipulated and restricted in their growth. We will look at how such a model is developed as a partnership with biologists and the insights it has been able to provide.

Speaker

Esther Wershof is a third year PhD student at the Francis Crick Institute, having previously studied maths at undergraduate and postgraduate level at Warwick University. She took the leap into medical research after realising that stopping the spread of disease was fundamentally a mathematical problem – one that she wanted to contribute to solving. Her favourite thing about her PhD is getting to work with people who think very differently – (biologists, chemists and even artists), bouncing ideas off each other and marvelling as a synergy takes place. When she's not creating her own biological patterns, you can find Esther looking at other people’s patterns in the Tate Modern. One of her most recent collaborations has lead to the construction of a virtual reality art experience where participants can climb inside and alter cell patterns in the body.

Meetup.com

Abstract

This paper presents a continuous-time equilibrium model of TWAP trading and liquidity provision in a market with multiple strategic investors with intraday trading targets. We demonstrate analytically that there are infinitely many Nash equilibria. We solve for the welfare-maximizing equilibrium and the competitive equilibrium and show that these equilibria are different. The model is computationally tractable, and we provide a number of numerical illustrations.

(Joint with Jin Hyuk Choi and Duane J. Seppi)

Speaker

Kasper Larsen is an Associate Professor in Mathematics at Rutgers University with a Ph.D. in mathematics from the University of Southern Denmark. Most of his research is in Mathematical Finance with various applications to model stability, equilibrium price formation, and optimal liquidation in equilibrium models. His work has been published in journals such as the Journal of Financial Economics, Mathematical Finance, Finance and Stochastics, Annals of Applied Probability, and Journal of Economic Theory. Part of his research has been supported by the National Science Foundation (NSF).

London (City) — Ian Khrashchevskyi — Is there a reward for macroeconomic risk in higher moment risk premia?

Ian Khrashchevskyi

Date and Time

Wednesday 13 June 2018, at 6:30 p.m.

Venue

City University Club, 42 Crutched Friars, London, UK

Meetup.com

Abstract

This talk investigates whether the risk premia associated with higher moments such as variance, skewness and kurtosis, price in the macroeconomic risk. The paper uses options prices to estimate such premia in a model-free way and employs a large set of macroeconomic variables to answer the question. The findings suggest that higher moments risk premia bear compensation for macroeconomic risk and identify two main driving factors: market risk and financial constraints. We do not find any predictability in higher moment risk premia.

Speaker

Ian Khrashchevskyi is a PhD student in finance at Stockholm Business School. The title of his dissertation is “Essays on risk in investment strategies”. His main research interests lie within areas of asset pricing, behavioral finance and risk management.

Venue

Meetup.com

Abstract

The availability of powerful Natural Language Processing techniques led to the emergence of AI tool that reads and interprets unstructured textual information, such as news and social media messages. The sentiment of finance-related content influences trading and investment decisions of players in financial markets and hence, moves the prices of assets.
Dr. Svetlana Borovkova has been working for several years in the area of sentiment analysis and its relation to financial markets; applications of sentiment analysis range from commodity trading to systemic risk to quantitative investment strategies.
In this talk, Dr. Borovkova will give an overview of this exciting field and show, among other things, how media sentiment can be used to forecast global financial distress, to generate sector and country rotation investment strategies and to help enhance machine learning applications to intraday trading.

Speaker

Dr. Svetlana Borovkova is an Associate Professor of Quantitative Finance in Vrije Universiteit Amsterdam and Head of Quantitative Modelling in risk advisory firm Probability & Partners. She is renowned for her work in, among others, sentiment analysis in finance, commodity market modelling and systemic risk. Dr Borovkova has over 50 scientific and professional publications in quant finance and risk management. She is a frequent speaker at a major industry events such as Risk Minds and Quant Minds.

Venue

Meetup.com

Abstract

Ivan Zhdankin: Key Aspects of Machine Learning in Finance

Abstract: The talk is about the fundamentals one should know when building machine learning models. The main issues discussed are overfitting, underfitting, bias and variance and how we approach those challenges, namely with cross-validation, bootstrap and regularization techniques. During the talk we will go through classic examples demonstrating those issues and will solve them by the listed techniques. At the end of the talk we will build a ML model going through every step of development: the model estimates the market impact in the crypto currency market.

Douglas Vieira: Microstructure of Option Prices: Reconciling Small and Long Time Dynamics

Abstract: Even though the option pricing literature is very rich, little is known about its dynamics at a microstructure level. In fact, the modelling of the microstructure of option price dynamics is a bit paradoxical. The price of an option reflects the expectation of its underlying asset price and volatility over a large time scale. Hence, while studying the microstructure of option, the global structure of its underlying price process cannot be ignored. Using asymptotic arguments, we address this very paradox in two directions. First, we distinguish the features of the global 'fundamental' dynamics are preserved locally from those that are lost. Second, we add microstructure effects to this 'fundamental' price process and show that they vanish at a larger time scale. The main outcome of this analysis is to precisely identify in which form the option price depends on volatility at small time scales.

Venue

Meetup.com

Abstract

Finding a universal market impact formula remains one of the most fascinating puzzles in finance. This paper reviews two possible approaches for imposing restrictions on this formula. First, restrictions can be obtained from a system of economic equations using trading volume and volatility, as suggested by Kyle and Obizhaeva (2017b). Second, restrictions can be derived using dimensional analysis and leverage neutrality, as suggested by Kyle and Obizhaeva (2017a). Except for the knife-edged case of the square root market impact function, additional assumptions related to market microstructure invariance are needed to apply the same market impact formula to all assets simultaneously. This results in a tightly parameterized universal market impact formula suitable for empirical testing.

(Joint work with Anna A. Obizhaeva)

Speaker

Albert S. Kyle has been the Charles E. Smith Chair Professor of Finance at the University of Maryland’s Robert H. Smith School of Business since 2006. He earned is B.S. degree in mathematics from Davidson College (summa cum laude, 1974), studied philosophy and economics at Oxford University as a Rhodes Scholar from Texas (Merton College, 1974-1976, and Nuffiled College, 1976-1977), and completed his Ph.D. in economics at the University of Chicago in 1981. He has been a professor at Princeton University (1981-1987), the University of California Berkeley (1987-1992), and Duke University (1992-2006).

Kyle’s research focuses on market microstructure, including topics such as high frequency trading, informed speculative trading, market manipulation, price volatility, the informational content of market prices, market liquidity, and contagion.

He is a Fellow of the American Finance Association in (2013) and a Fellow of the Econometric Society (2002) . He has been a board member of the American Finance Association (2004-2006). He holds an honorary doctoral degree from the Stockholm School of Economics (2013). He was a staff member of the Presidential Task Force on Market Mechanisms (Brady Commission, 1987), a consultant to the SEC (Office of Inspector General), CFTC, and U.S. Department of Justice, a member of NASDAQ’s economic advisory board (2004-2007), a member of the FINRA economic advisory board (2010-2014), and a member of the CFTC’s Technology Advisory Committee (2010-2012).

Disclaimer

This a joint IAQF/Thalesians seminar, and not an instructional program of New York University.

Venue

Meetup.com

Abstract

Because energy markets were liberalised only in the last decades, modelling electricity prices is a relatively new topic in mathematics and economics. Electricity is considered a commodity with unique futures that make the use of standard tools of financial mathematics difficult or even impossible. During my talk we will explore some of the methods used in energy finance, their applications and limitations. We will also study one model in more detail, namely a three-factor model of German electricity prices that takes into account the impact of wind energy production.

Speaker

Paula Rowińska is a PhD student of the Mathematics of Planet Earth Centre for Doctoral Training at Imperial College London. Her research interests include statistics and stochastic modelling applied to Earth sciences, ecology and finance. Currently she is studying how renewable energy sources influence electricity prices. She also actively engages in science communication, for example by blogging about science: www.paularowinska.wordpress.com. In 2017 she gave a TEDx talk about looking for exciting maths in everyday life.

Meetup.com

Abstract

With regulatory pushes for CCP-based trading and for the application of initial margin in OTC trading, the funding and counterparty credit risk areas are undergoing a fundamental transition. In this talk, we discuss practical ways to measure credit and funding costs in the new market reality. We present new theory for certain classes of locally elliptical processes, and show how this theory can be used to generalize Ito processes and to conveniently calculate CVA and MVA in a fat-tailed setting. We cover both OTC bilateral trading, as well as the more complicated situation where a bank trades its house account as a clearing member of a CCP.

(Joint work with Anna A. Obizhaeva)

Speaker

Leif B. G. Andersen is the Global Co-Head of the Quantitative Strategies Group at Bank of America Merrill Lynch. He holds MSc’s in Electrical and Mechanical Engineering from the Technical University of Denmark, an MBA from University of California at Berkeley, and a PhD in Finance from University of Aarhus Business School. He was the co-recipient of Risk Magazine’s 2001 and 2018 Quant of the Year Award, and has worked for a quarter century as a quantitative researcher in the derivatives pricing area. He has authored influential research papers and books in all areas of quantitative finance, including the three-volume monograph “Interest Rate Modelling’’ (co-authored with Vladimir Piterbarg). He is an Associate Editor of Journal of Computational Finance, and is an Adjunct Professor at Carnegie Mellon’s Tepper School of Business and at NYU’s Courant Institute.

He is a Fellow of the American Finance Association in (2013) and a Fellow of the Econometric Society (2002) . He has been a board member of the American Finance Association (2004-2006). He holds an honorary doctoral degree from the Stockholm School of Economics (2013). He was a staff member of the Presidential Task Force on Market Mechanisms (Brady Commission, 1987), a consultant to the SEC (Office of Inspector General), CFTC, and U.S. Department of Justice, a member of NASDAQ’s economic advisory board (2004-2007), a member of the FINRA economic advisory board (2010-2014), and a member of the CFTC’s Technology Advisory Committee (2010-2012).

Disclaimer

This a joint IAQF/Thalesians seminar, and not an instructional program of New York University.

Venue

Meetup.com

Abstract

We consider an enhanced rough volatility model, designed to capture the smiles of VIX market instruments in a weighted rough Bergomi model framework, building on the work of Bergomi [2008, 2016], and De Marco [2018].

Calibration time being the bottleneck for rough volatility, we approach calibration of this model with a bouquet from the machine learning toolkit:

In particular, we discuss various machine learning methods that significantly reduce calibration time, fixing an arbitrary tolerance level for calibration errors. We also study the dynamical evolution of optimal parameters over time, and the influence of the intital guesses on the calibration performance in this context. As a byproduct of our results, we re-confirm that volatility is rough, calibration performance being best for very small Hurst parameters in all market scenarios.

The talk will be presented jointly with Amir Sani (Imperial College London)

ANNOUNCEMENT

We are delighted to announce a new co-operation with Techila Technologies. Techila Technologies appreciates the great work that Thalesians are doing, including the meetups with excellent presentations in many different fields. We all know that meetups are nicer when there is some food and drinks, Techila Technologies has promised to make sure that we Thalesians will have drinks and food in the London meetups this spring. This arrangement commences with tonight's talk, so see you there!

Techila Distributed Computing Engine is a next generation compute grid. It is a quant-friendly solution that connects fast and scalable computing directly to PyCharm, MATLAB, RStudio, Eclipse, and other popular quantitative development environments. This makes it an ideal solution also in the R&D prototyping and development. Would you like to know, how you can use Techila in your own code? https://www.youtube.com/watch?v=KZyfOYV94MI

Venue

Meetup.com

Abstract

The recent financial crisis has focused attention on identifying and measuring systemic risk. In this paper, we propose a novel approach to estimate the portfolio composition of banks as function of daily interbank trades and stock returns. While banks’ assets are reported to regulators and/or the public at relatively low frequencies (e.g. quarterly or monthly), our approach estimates bank asset holdings at higher frequencies which allows us to derive precise estimates of (i) portfolio concentration within each bank—a measure of diversification—and (ii) common holdings across banks—a measure of market susceptibility to propagating shocks. We find evidence that systemic risk measures derived from our approach lead, in a forecasting sense, several commonly used systemic risk indicators.

Speaker

Celso Brunetti is the chief of the Systemic Financial Institutions and Markets section, Division of Research and Statistics, at the Board of Governors of the Federal Reserve System. His current responsibilities include, among other things, conducting policy analysis and economic research on financial stability as it relates to systemically important financial institutions, with a particular focus on non-banks, and the markets in which they operate. His research agenda covers four main topics: (i) Network analysis of financial markets, (ii) linkages between financial markets and the macroeconomy, (iii) market microstructure, and (iv) commodity markets.

Before joining the FED, Celso spent his professional career in academia with appointments at Johns Hopkins University, University of Pennsylvania and University of Edinburgh where he taught Statistics, Corporate Finance, Derivatives and Financial Risk Management. From 2008 to 2011, he has been a consultant at the Commodity Futures Trading Commission.

He received A. B. degree in economics and banking from the Catholic University in Milan, Italy, and a Master of Science degree in economics from Bocconi University in Milan, Italy. In 1999 he completed his PhD in economics at the University of London, the UK.

Celso published several papers in academic journals such as the Review of Financial Studies, Journal of Financial and Quantitative Analysis, Econometrics Journal and Journal of Financial Markets.

Disclaimer

This a joint IAQF/Thalesians seminar, and not an instructional program of New York University.

Venue

Meetup.com

Abstract

Portfolio optimization in an uncertain market environment can be modeled via a stochastic Sharpe ratio process, where the uncertainty may arise from the drift or volatility, or both, of the risky asset. The impact of the uncertainty can be approximately characterized through the concept of implied Sharpe ratio, analogous to the much-studied implied volatility in option pricing. We show how this can be used to produce adjustments to the Merton optimal investment strategy that account for principal features of the stochastic market environment, and how the implied volatility skew can be used to infer parameters for this strategy.

Speaker

Ronnie Sircar is a Professor of Operations Research and Financial Engineering at Princeton University, and is affiliated with the Bendheim Center for Finance, the Program in Applied and Computational Mathematics and the Andlinger Center for Energy and the Environment. He received his doctorate from Stanford University, and taught for three years at the University of Michigan in the Department of Mathematics. He has received continuing National Science Foundation research grants since 1998. He was a recipient of the E-Council Excellence in Teaching Award for his teaching in 2002, 2005 and 2006, and the Howard B. Wentz Jr. Junior Faculty Award in 2003. His research interests center on Financial Mathematics, stochastic volatility models, energy markets and exhaustible resources, credit risk, asymptotic and computational methods, portfolio optimization and stochastic control problems, and stochastic differential games. He is a co-author of the book "Multiscale Stochastic Volatility for Equity, Interest-Rate and Credit Derivatives", published by Cambridge University Press in 2011, and was founding co-editor-in-chief of the SIAM Journal on Financial Mathematics, from 2009-2015.

Disclaimer

This a joint IAQF/Thalesians seminar, and not an instructional program of New York University.

Venue

Meetup.com

Abstract

Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finace. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project --- Marvin Minsky, Nathaniel Rochester, and Claude Shannon --- was more symbolic than numerical, and often used the language of logic. Recent advances in ML --- especially Deep Learning --- are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.

Speaker

Dr. Paul A. Bilokon is CEO and Founder of Thalesians Ltd. He has previously served as Director at Deutsche Bank, where he ran the global credit and core quant teams, part of Markets Electronic Trading (MET) group. He is one of the pioneers of electronic trading in credit, including indices, single names, and cash, and has worked in e-trading, derivatives pricing, and quantitative finance at bulge bracket institutions, including Morgan Stanley, Lehman Brothers, Nomura, and Citigroup. His more than a decade-long career spans many asset classes: equities, FX spot and options, rates and credit.

Paul has graduated from Christ Church, Oxford, with a distinction, and twice from Imperial College London. The domain-theoretic framework for continuous-time stochastic processes, developed with Prof. Abbas Edalat, earned him a PhD degree and a prestigious LICS paper. Paul's other academic interests include stochastic filtering and machine learning. He is an expert developer in C++, Java, Python, and kdb+/q, with a special interest in high performance scientific computing.

His interests in philosophy and finance led him to formulate the vision for and found Thalesians, a consultancy and think tank of dedicated professionals working in quant finance, economics, mathematics, physics and computer science, the focal point of a community with over 2,500 members worldwide. Thalesians was co-founded with two of Paul's friends and colleagues, Saeed Amen and Matthew Dixon.

Dr. Bilokon is a joint winner of the Donald Davis Prize (2005), winner of the British Computing Society Award for the Student Making the Best Use of IT (World Leadership Forum's SET award, 2005), Ward Foley Memorial Scholarship (2001), two University of London High Achiever Awards (in mathematics and physics, 1999); a Member of the British Computer Society, Institution of Engineering and Technology, and European Complex Systems Society; Associate of the Securities and Investment Institute, and Royal College of Science; and a frequent speaker at premier conferences such as Global Derivatives, alphascope, LICS, and Domains.

Venue

Meetup.com

Abstract

Unlike securities markets, the residential property market is relatively inefficient and displays obvious return regularities which are of interest to that minority of participants who are primarily interested in capital gain rather than lifestyle factors, which includes many first time buyers. The analysis proceeds by first estimating over repeat sales indexes at the postcode district or sector level, applying a factor model familiar to equity quants, revaluing over 10M properties to the last month-end, and interactively mapping the risk-decomposed returns and valuation in £/m2. Along the way, estimates are Bayes-conditioned and key parameters are tuned by cross-validation to minimise out-of-sample error. The distribution of postcode factors is shown with 3D interactive graphics and - unlike securities markets - some surprisingly intuitive and practical conclusions for efficient portfolio construction do emerge. Three approaches to return forecasting are shown, including VAR and cyclical, together with applications in screening key metrics: location, momentum, value, risk, and liquidity - where possible down to the postcode unit level. An interactive online mapping/screening tool URL is provided to participants.

Speaker

Giles Heywood - Amber Alpha in association with Seven Dials Fund Management
Giles Heywood is founder and CEO of Amber Alpha Limited, delivering alpha and risk management products to quantitative equity hedge fund and private equity specialists. Previously he headed hedge fund origination at a ABN AMRO Asset Management, and lead the quantitative research team at Gartmore. As a sell-side quant on the prizewinning Commerzbank team he developed an early GPL-licenced R package on CRAN. Having started his career in high-frequency geophysical consulting after graduating from Cambridge, he now primarily focuses on development and testing of highly diversified mid-to-low frequency systematic products, including residential property.

Date and Time

Venue

Meetup.com

Abstract

Copula models are usually used in order to capture multiple default contingent payoffs. As such, this standard approach fails to capture credit curve gamma and cross-gamma impacts arising from non-linear dependency on CDS spreads. Neglecting these impacts lead to unexplained P&L swings for the delta hedged portfolios, as it had been demonstrated during crisis. Our approach retains systemic default feature while taking into account a joint dynamics of credit spreads.

Speaker

Alexander Veygman had been a leading fixed income desk quantitative analyst at HSBC for the past 12 years working on valuation of various kinds of vanilla and exotic interest rates and credit hybrids derivatives. His scope of interest includes researching numerical methods to simultaneously incorporate multiple market observables to develop practical models ready to be used by fixed income desks. He holds and MS degree from NYU in Statistics & Operations Research/Math in Finance.

Puzzles

Masses and Buckets

You have M masses, which you want to distribute across N buckets "as uniformly as possible". By this I mean that you are trying to minimise , where bk is the sum of the masses in the k-th bucket. How would you achieve this?

To make this a little bit more concrete, suppose that I give you 20 masses, e.g. 23, 43, 12, 54, 7, 3, 5, 10, 54, 55, 26, 9, 9, 43, 54, 1, 8, 6, 38, 33. There are 4 buckets. How would you distribute the masses?

Please send your answers to paul, who happens to be at thalesians.com.